Abstract
This paper focuses on exploring personalized multi-task learning approaches for collaborative filtering towards the goal of improving the prediction performance of rating prediction systems. These methods first specifically identify a set of users that are closely related to the user under consideration (i.e., active user), and then learn multiple rating prediction models simultaneously, one for the active user and one for each of the related users. Such learning for multiple models (tasks) in parallel is implemented by representing all learning instances (users and items) using a coupled user-item representation, and within error-insensitive Support Vector Regression (ε-SVR) framework applying multi-task kernel tricks. A comprehensive set of experiments shows that multi-task learning approaches lead to significant performance improvement over conventional alternatives.
Original language | English (US) |
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Pages (from-to) | 269-284 |
Number of pages | 16 |
Journal | Journal of Machine Learning Research |
Volume | 13 |
State | Published - 2010 |
Event | 2nd Asian Conference on Machine Learning, ACML 2010 - Tokyo, Japan Duration: Nov 8 2010 → Nov 10 2010 |
Keywords
- Collaborative filtering
- Multi-task learning